A Fuzzy Logic Prompting Framework for Large Language Models in Adaptive and Uncertain Tasks

• URL: http://arxiv.org/abs/2508.06754v1
• Date: Fri, 08 Aug 2025 23:50:48 GMT
• Title: A Fuzzy Logic Prompting Framework for Large Language Models in Adaptive and Uncertain Tasks
• Authors: Vanessa Figueiredo,
• Abstract summary: We introduce a modular prompting framework that supports safer and more adaptive use of large language models (LLMs) across dynamic, user-centered tasks.<n>Our method combines a natural language boundary prompt with a control schema encoded with fuzzy scaffolding logic and adaptation rules.<n>In a simulated intelligent tutoring setting, the framework improves scaffolding quality, adaptivity, and instructional alignment across multiple models, outperforming standard prompting baselines.
• Score: 2.1756081703276
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a modular prompting framework that supports safer and more adaptive use of large language models (LLMs) across dynamic, user-centered tasks. Grounded in human learning theory, particularly the Zone of Proximal Development (ZPD), our method combines a natural language boundary prompt with a control schema encoded with fuzzy scaffolding logic and adaptation rules. This architecture enables LLMs to modulate behavior in response to user state without requiring fine-tuning or external orchestration. In a simulated intelligent tutoring setting, the framework improves scaffolding quality, adaptivity, and instructional alignment across multiple models, outperforming standard prompting baselines. Evaluation is conducted using rubric-based LLM graders at scale. While initially developed for education, the framework has shown promise in other interaction-heavy domains, such as procedural content generation for games. Designed for safe deployment, it provides a reusable methodology for structuring interpretable, goal-aligned LLM behavior in uncertain or evolving contexts.

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